OBJECTIVE: This study aimed to explore orthopaedic patients' and families' experiences with artificial intelligence (AI)-driven chatbots for perioperative health information, focusing on usability, effectiveness and perceptions.
Accurately estimating kinetic metrics, such as braking and propulsion forces, in real-world running environments enhances our understanding of performance, fatigue, and injury. Wearable inertial measurement units (IMUs) offer a potential solution to ...
Uncontrolled hypertension (HTN) increases the risk of adverse health events. This study aimed to identify key predictors of uncontrolled HTN in 1,308 Mexican adults with a prior diagnosis of HTN who were undergoing pharmacological treatment. We utili...
PURPOSE: To investigate the effectiveness of an integrated deep-learning (DL) algorithm, the Mixture of Radiological Findings Specific Experts (MoRFSE), in breast cancer classification by imitating the diagnostic decision-making process of radiologis...
BACKGROUND: Professional identity plays a critical role in the career development of male postgraduate nursing students, particularly in contexts where gender imbalance and social stereotypes persist.
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...
BMC medical informatics and decision making
Sep 3, 2025
BACKGROUND AND OBJECTIVES: Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasib...
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high...
BACKGROUND: Digital therapeutics (DTx) show promise in bridging mental healthcare gaps. However, treatment selection often relies on availability and trial-and-error, prolonging suffering and increasing costs. Personalised prediction models could hel...
BACKGROUND: Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accurac...
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